


import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard




@slow
@require_flax
class A    (  unittest.TestCase	):






      def 			lowerCamelCase			(   self		:	int					)   ->		Any:
             """simple docstring"""




             super().tearDown()
             gc.collect()






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =FlaxStableDiffusionPipeline.from_pretrained(
                 'stabilityai/stable-diffusion-2'     ,  revision='bf16'     ,  dtype=jnp.bfloataa     ,  )

             _lowerCamelCase			:						int            ='A painting of a squirrel eating a burger'
             _lowerCamelCase			:						Optional[Any]            =jax.device_count()
             _lowerCamelCase			:						Tuple            =num_samples * [prompt]
             _lowerCamelCase			:						List[str]            =sd_pipe.prepare_inputs(UpperCamelCase__					)

             _lowerCamelCase			:						List[Any]            =replicate(UpperCamelCase__					)
             _lowerCamelCase			:						Any            =shard(UpperCamelCase__					)

             _lowerCamelCase			:						str            =jax.random.PRNGKey(0					)
             _lowerCamelCase			:						Tuple            =jax.random.split(UpperCamelCase__     ,  jax.device_count()					)

             _lowerCamelCase			:						Tuple            =sd_pipe(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  num_inference_steps=25     ,  jit=UpperCamelCase__					)[0]
             assert images.shape == (jax.device_count(), 1, 768, 768, 3)

             _lowerCamelCase			:						Union[str, Any]            =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
             _lowerCamelCase			:						List[str]            =images[0, 253:256, 253:256, -1]

             _lowerCamelCase			:						Optional[Any]            =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
             _lowerCamelCase			:						Optional[Any]            =jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]					)
             print(F'''output_slice: {output_slice}'''					)
             assert jnp.abs(output_slice - expected_slice					).max() < 1E-2






      def 			lowerCamelCase			(   self		:	str					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            ='stabilityai/stable-diffusion-2'
             _lowerCamelCase      ,	_lowerCamelCase			:						str            =FlaxDPMSolverMultistepScheduler.from_pretrained(UpperCamelCase__     ,  subfolder='scheduler'					)
             _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =FlaxStableDiffusionPipeline.from_pretrained(
                 UpperCamelCase__     ,  scheduler=UpperCamelCase__     ,  revision='bf16'     ,  dtype=jnp.bfloataa     ,  )
             _lowerCamelCase			:						str            =scheduler_params

             _lowerCamelCase			:						Union[str, Any]            ='A painting of a squirrel eating a burger'
             _lowerCamelCase			:						List[Any]            =jax.device_count()
             _lowerCamelCase			:						List[Any]            =num_samples * [prompt]
             _lowerCamelCase			:						Optional[Any]            =sd_pipe.prepare_inputs(UpperCamelCase__					)

             _lowerCamelCase			:						str            =replicate(UpperCamelCase__					)
             _lowerCamelCase			:						Optional[Any]            =shard(UpperCamelCase__					)

             _lowerCamelCase			:						Any            =jax.random.PRNGKey(0					)
             _lowerCamelCase			:						str            =jax.random.split(UpperCamelCase__     ,  jax.device_count()					)

             _lowerCamelCase			:						Optional[int]            =sd_pipe(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  num_inference_steps=25     ,  jit=UpperCamelCase__					)[0]
             assert images.shape == (jax.device_count(), 1, 768, 768, 3)

             _lowerCamelCase			:						Optional[int]            =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
             _lowerCamelCase			:						Any            =images[0, 253:256, 253:256, -1]

             _lowerCamelCase			:						str            =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
             _lowerCamelCase			:						int            =jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]					)
             print(F'''output_slice: {output_slice}'''					)
             assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""image_processor""", """tokenizer"""]
  a__       			=						"""BridgeTowerImageProcessor"""
  a__       			=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__       )
  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Union[bool, str, PaddingStrategy] = False     ,				UpperCamelCase__     :					Union[bool, str, TruncationStrategy] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				**UpperCamelCase__     :					Dict     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			self.tokenizer(
        text=UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				stride=UpperCamelCase__     ,				pad_to_multiple_of=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				return_overflowing_tokens=UpperCamelCase__     ,				return_special_tokens_mask=UpperCamelCase__     ,				return_offsets_mapping=UpperCamelCase__     ,				return_length=UpperCamelCase__     ,				verbose=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    # add pixel_values + pixel_mask
    __magic_name__								=			self.image_processor(
        UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				do_normalize=UpperCamelCase__     ,				do_center_crop=UpperCamelCase__     ,				**UpperCamelCase__       )
    encoding.update(UpperCamelCase__       )

    return encoding
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__     ,				**UpperCamelCase__       )





  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer.model_input_names
    __magic_name__								=			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

